I have a numpy array data. Here is its shape:

data.shape

(223,12,437)

I want to make a dataframe out of this array. I want the data frame to have:

• 223 rows
• 1 column
• Each element is a np.array with shape (12,437).

When I run:

pd.DataFrame(data)

I get this error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-55-cd168f76d566> in <module>
----> 1 pd.DataFrame(data)

~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
    676                     dtype=dtype,
    677                     copy=copy,
--> 678                     typ=manager,
    679                 )
    680 

~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/construction.py in ndarray_to_mgr(values, index, columns, dtype, copy, typ)
    302         # by definition an array here
    303         # the dtypes will be coerced to a single dtype
--> 304         values = _prep_ndarray(values, copy=copy)
    305 
    306     if dtype is not None and not is_dtype_equal(values.dtype, dtype):

~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/construction.py in _prep_ndarray(values, copy)
    553         values = values.reshape((values.shape[0], 1))
    554     elif values.ndim != 2:
--> 555         raise ValueError(f"Must pass 2-d input. shape={values.shape}")
    556 
    557     return values

ValueError: Must pass 2-d input. shape=(223, 12, 437)

Why is this? Doesn't the 0th element in the shape tuple (223,) contain the information for how many rows to make the df?

What should I do instead?

CodePudding user response：

Create a new axis to transform your array from (223, 12, 247) to (223, 1, 12, 247):

data = np.random.random((223, 12, 247)
df = pd.DataFrame.from_records(m[:, None])  # None or np.newaxis

>>> df
                                                     0
0    [[0.8545012287346487, 0.094059810377082, 0.470...
1    [[0.6645975722200621, 0.567675394564319, 0.459...
2    [[0.4474745169474814, 0.4823023696009986, 0.15...
3    [[0.31251548689763453, 0.7357607646976804, 0.2...
4    [[0.4522848739922676, 0.042101609272210516, 0....
..                                                 ...
218  [[0.0085917543787426, 0.9542525347386845, 0.37...
219  [[0.03667682481611034, 0.14416094093914922, 0....
220  [[0.05475820484458771, 0.8582654934659678, 0.0...
221  [[0.641864950301045, 0.9591725641855815, 0.103...
222  [[0.5027508533463017, 0.15570208093984983, 0.4...

>>> df.shape
(223, 1)

>>> df.iloc[0, 0].shape
(12, 247)

Note: (223, 1) and (12, 247) give you a shape of (223, 1, 12, 247)